Detection and Classification of Transmission Lines Faults using FFT and K-Nearest Neighbour Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.6 · Issue 8

Published

March 2018

Access

Open Access

Licence

CC BY-NC-SA 4.0

Nilesh S.Wani Dr. R. P. Singh Dr. M. U. Nemade

This paper presents a strategy for identifying the fault and its classification, in an electrical power distribution system. The strategy is based on a very simple technique, known as the k nearest neighbours (KNN), which simply estimates a distance between the characteristics that describe the data to be classified. When a new datum is presented to the proposed algorithm, it is classified with the same type of the example that is determined to be the closest one. For the creation of the mathematical model it is essential to have a database. The database consists of input data and output data, the input data are the detail coefficients obtained from the decomposition of the current and voltage signals using the Fourier Transform. Meanwhile, the output data are the labels assigned and with which the model can identify and classify the different types of faults. Both current signals and voltage signals are generated based on an extensive simulation of faults along the longest transmission line that has a test system.

Nilesh S.Wani, Dr. R. P. Singh, Dr. M. U. Nemade (2018). Detection and Classification of Transmission Lines Faults using FFT and K-Nearest Neighbour Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.6, Issue 8. ISSN: 2319-4863.

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